报告题目：Functional calibration under non-probability survey sampling
时 间：2024年6月7日（星期五）10：00
地 点：科研楼18号楼1102
主 办：数学与统计学院、分析数学及应用教育部重点实验室、福建省分析数学及应用重点实验室、统计学与人工智能福建省高校重点实验室、福建省应用数学中心（福建师范大学）
参加对象：感兴趣的老师和研究生
报告摘要：Non-probability sampling is prevailing in survey sampling, but it leads to erroneous inference if its selection bias is overlooked. By integrating a non-probability sample and a probability sample, we propose a nonparametric method to adjust such selection bias through calibrating marginal means of functions in a reproducing kernel Hilbert space. Theoretical properties, including consistency and a limiting distribution, are established. Compared with existing ones, the proposed estimator is more robust since no parametric assumption is made for both regression and response models associated with the non-probability sample. Numerical experiments show that the proposed estimator outperforms its competitors. The proposed method is applied to the second-round economic census data in China as an empirical illustration.
报告人简介：王中雷为美国爱荷华州立大学统计学博士，厦门大学经济学科副教授。多篇研究成果发表于Journal of the Royal Statistical Society Series B (Statistical Methodology)、Journal of the American Statistical Association、Biometrika以及Nature Communications等统计学和自然科学期刊。主持国家自然科学基金青年项目1项，参与国家重点研发计划、国家自然科学基金重点基金等。